Podcasts about OSI

Throwbacks are where I re-release old episodes from the archives. So don't worry if you have heard it already, as 'New episodes' will continue to come out on Sundays. To get some of the old episodes heard.~~~Our first guest tonight is Ryan in Arizona sharing a couple of his Out-of-body experiences, Ryan had a terrifying experience after hitting his head on his bathtub, he passed out and remembers travelling at insane speeds in some sort of weird colourful tunnel. Then we move on to Angela in North Carolina, She's a survivor of child abuse, was kidnapped and illegally adopted into a military CIA family.More information on this episode on the podcast website:https://ufochroniclespodcast.com/ep-258-it-was-terrifying-fort-bragg/Want to share your encounter on the show?Email: UFOChronicles@gmail.comOr Fill out Guest Form:https://forms.gle/uGQ8PTVRkcjy4nxS7Podcast Merchandise:https://www.teepublic.com/user/ufo-chronicles-podcastHelp Support UFO CHRONICLES by becoming a Patron:https://patreon.com/UFOChroniclespodcastX: https://x.com/UFOchronpodcastAll Links for Podcast:https://linktr.ee/UFOChroniclesPodcastThank you for listening!Like share and subscribe it really helps me when people share the show on social media, it means we can reach more people and more witnesses and without your amazing support, it wouldn't be possible.Become a supporter of this podcast: https://www.spreaker.com/podcast/ufo-chronicles-podcast--3395068/support.

Throwbacks are where I re-release old episodes from the archives. So don't worry if you have heard it already, as 'New episodes' will continue to come out on Sundays. To get some of the old episodes heard.~~~Our first guest tonight is Ryan in Arizona sharing a couple of his Out-of-body experiences, Ryan had a terrifying experience after hitting his head on his bathtub, he passed out and remembers travelling at insane speeds in some sort of weird colourful tunnel. Then we move on to Angela in North Carolina, She's a survivor of child abuse, was kidnapped and illegally adopted into a military CIA family.More information on this episode on the podcast website:https://ufochroniclespodcast.com/ep-258-it-was-terrifying-fort-bragg/Want to share your encounter on the show?Email: UFOChronicles@gmail.comOr Fill out Guest Form:https://forms.gle/uGQ8PTVRkcjy4nxS7Podcast Merchandise:https://www.teepublic.com/user/ufo-chronicles-podcastHelp Support UFO CHRONICLES by becoming a Patron:https://patreon.com/UFOChroniclespodcastX: https://x.com/UFOchronpodcastAll Links for Podcast:https://linktr.ee/UFOChroniclesPodcastThank you for listening!Like share and subscribe it really helps me when people share the show on social media, it means we can reach more people and more witnesses and without your amazing support, it wouldn't be possible.Become a supporter of this podcast: https://www.spreaker.com/podcast/ufo-chronicles-podcast--3395068/support.

After a scarily successful Friday night, JAMES GONIS, SHAWN SHERIDAN, LARRY STROTHE, and MATT WEINHOLD return to the Pasadena Convention Center for another day of horror-themed hijinks! If it's Saturday, it's... MONSTERPALOOZA 2026 PART 2!!! The second day of this epic show provided us with another diabolical dollop of vendors, artists, celebrities, panels, cosplayers, SFX makeup demonstrations, and more! And by more, we mean a dazzling array of on-the-spot interviews at the MONSTER PARTY booth! Joining us that afternoon were a tsunami of exciting guests, including SPOOKYWORLD theme park founder DAVID BERTOLINO, writer/director/producer DANNY WOLF, sci-fi film director GARO SETIAN (SPACE WARS: QUEST FOR THE DEEPSTAR, AUTOMATION), trailer director HUNTER SETIAN, actor and film soundtrack journalist DANIEL SCHWEIGER (PHANTASM: RAVAGER, JOHN DIES AT THE END), magazine editor/journalist/film director DAVID WEINER (IN SEARCH OF DARKNESS), HORROR NERDS COMEDY PODCAST hosts SAMANTHA HALE and BONNIE MARIE WILLIAMS, 2 BLACK GUYS TALK GODZILLA PODCAST host AARON NEUWIRTH, author/comedian/Jumbo Machinder collector TOM FRANCK (JUMBO TITAN X), fantasy illustrator MIRANDA FRANCK, and legendary horror host MR. LOBO (CINEMA INSOMNIA, OSI). Whew! Of course, what would a day of MONSTERPALOOZA be without visits from fans, friends, and family, including a visit from Larry's niece, who gave us all a schooling on PROJECT HAIL MARY. Did she make some good points? Yes. Did she change any minds? What do you think? LISTEN NOW TO WHAT YOU MISSED THIS LAST MAY, AND START MAKING PLANS TO ATTEND MONSTERPALOOZA IN 2027. MONSTER PARTY WILL BE WAITING TO GREEEEET YOU!

Hoy reflexionamos sobre el amor propio y la importancia de tratarnos con el mismo cariño, paciencia y cuidado que solemos darle a las personas que más queremos. Un episodio para recordarte que tú también mereces ocupar un lugar importante en tu propia vida.A lo largo de estos 3 años de Durmiendo Podcast, hemos compartido episodios que les han ayudado muchísimo. Por eso, hoy traemos de vuelta las herramientas que más han resonado con ustedes y que les han acompañado a cerrar su día con calma

The scope of Kevin Moore's genius begins with Dream Theater's “Space-Dye Vest” and concludes with OSI's Fire Make Thunder. We try to use the g-word sparingly around here, but truly, Moore's singular artistic vision is something we attempt to hail and dissect here, for our 147th episode. Join us in celebrating this specific, singular talent. Note I: Hunter mentions “library music” in response to Chroma Key's music on the Graveyard Mountain Home album. You can learn more about library music here: https://en.wikipedia.org/wiki/Production_music Note II: The story we relate from Chris Warunki, at this end of this episode, about his very lucrative sale of Kevin Moore's pre-Chroma Key release, This is a Recording: Demos 1994-97, is given evidence here: https://www.discogs.com/release/4238872-Kevin-Moore-This-Is-A-Recording-Demos-1994-97 Note III: Jeff's Voivod book, ‘Always Moving: The Strange Multiverse of Voivod,' is now available. You can purchase a copy of the 540-page, 3-pound behemoth HERE: radicalresearch.org/voivod Note IV: The Radical Research Patreon page is now set up and ready for your patronage. We are offering tiered subscription levels for those who want a set-it-and-forget-it donation option. As ever, if you choose to support us, we are humbled and grateful! patreon.com/RadicalResearchPodcast Music cited in order of appearance: Dream Theater, “Ytse Jam” (When Dream and Day Unite, 1989) Dream Theater, “Wait for Sleep” (Images and Words, 1992) Dream Theater, “Space-Dye Vest” (Awake, 1994) Kevin Moore, “Space-Dye Vest” (demo, 1994) Fates Warning, “A Pleasant Shade of Gray, Part VIII” (A Pleasant Shade of Gray, 1997) Chroma Key, “Colorblind” (Dead Air for Radios, 1998) Chroma Key, “On the Page” (Dead Air for Radios, 1998) Chroma Key, “Astronaut Down” (You Go Now, 2000) Chroma Key, “Come In, Over” (Graveyard Mountain Home, 2004) OSI, “Hello, Helicopter” (Office of Strategic Influence, 2003) OSI, “Go” (Free, 2006) OSI, “Terminal” (Blood, 2009) OSI, “Radiologue” (Blood, 2009) Radical Research is a conversation about the inner- and outer-reaches of rock and metal music. This podcast is conceived and conducted by Jeff Wagner and Hunter Ginn. Though we consume music in a variety of ways, we give particular privilege to the immersive, full-album listening experience. Likewise, we believe that tangible music formats help provide the richest, most rewarding immersions and that music, artwork, and song titles cooperate to produce a singular effect on the listener. Great music is worth more than we ever pay for it.

OSI – otte års relativ ligegyldig italiensk industrihistorieDen måtte komme. Bilsnobbernes måske mest kedelige podcast. Den du ikke behøver lytte til.Med mindre, altså, du vil høre Stefan Kaas og Adam Estrup bevæge sig ind i den let triste italienske automotive industrifortælling om Officine Stampaggi Industriali, OSI.Etableret i 1960 i Torino af en tidligere Ghia-chef for at bygge mindre serier for de store – Fiat, Ford & Alfa – får OSI en levetid på cirka otte år. I dem laver de lidt åbne Innocentier, et par sjove Alfaer, der hedder skarabæ, og cirka 10.000 underlige Michelotti-designede Ford Anglia.Den lille bilsmedie ender med at blive pænt berømmet for deres Ford OSI 20 M TS, der er en mindre kedelig tysk Taunus. Bilen er meget pæn i tre dele.Designeren var Sergio Sartorelli, der siden tegnede Fiat 126.OSIs medstifter var ud af Olivetti-familien, men det redder ikke OSI fra at gå konkurs i 1968. Og det var så det. Finito.

A veces nos alejamos tanto de quienes somos que terminamos persiguiendo expectativas, metas y versiones de vida que ni siquiera nos pertenecen. En este episodio hacemos una pausa para volver a nuestra esencia, escuchar nuestra propia voz y recordar que la autenticidad no se construye: se descubre cuando dejamos de intentar ser alguien más.A lo largo de estos 4 años de Despertando Podcast, hemos compartido episodios que les han ayudado muchísimo, y hoy queremos traerles de vuelta todas esas herramientas que han resonado con ustedes y cambiado sus mañanas ☀️.En este episodio hablamos de:Cómo reconectar con la persona que realmente eresLa diferencia entre encajar y sentir que pertenecesPor qué no necesitas cambiar para ser suficiente o valiosa/oSi quieres conocer más de Despertando Podcast síguenos en nuestras redes sociales:

Parce que… c'est l'épisode 0x306! Shameless plug 24 et 25 juin 2026 - Troopers 26 et 27 juin 2026 - leHACK 30 juin au 2 juillet 2026 - Pass the SALT 19 septembre 2026 - Bsides Montréal 20 au 26 septembre 2026 - BruCON 13 novembre 2026 - DEATHCon 16 au 19 novembre - European Cyber Week 1 au 3 décembre 2026 - Forum INCYBER - Canada 2026 24 et 25 février 2027 - SéQCure 2027 Notes IA ou Ghost in the shell Mythos Anthropic invites EU to access Mythos hacking tech Anthropic scales Claude Mythos to critical infrastructure in 15+ countries Anthropic Expands Project Glasswing Claude Mythos Preview to 150 New Organizations Kevin Beaumont: “Mythos is not great btw. Runni…” - Cyberplace Free AI model powers self-spreading worm in enterprise test network Instapassword Hackers Used Meta's AI Support Bot to Seize Instagram Accounts Instagram Meta AI Vulnerability Allegedly Enables Password Reset for Accounts Hackers duped Meta AI support chatbot to steal celebrity Instagram accounts Instagram Fixes Password Reset Flaw That Exposes User Emails and Phone Numbers Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked Kevin Beaumont: “How people hacked Meta account…” - Cyberplace Injecte moi ça ChatGPT for Google Sheets Exfiltrates Workbooks New Google Gemini Vulnerability Exploited via Prompt Injections from WhatsApp, Slack, and SMS New ChatGPT Lockdown Mode Limits Tools That Could Enable Data Exfiltration Irresponsable Florida sues OpenAI, Sam Altman after multiple ChatGPT-linked murders School shooting survivor sues AI gun detection firm after system failed to spot weapon AI Agents Get Their Own Directory Built Atop DNS Remove all LLM generated commits before people get hurt by this nonsense. · Issue #934 · RsyncProject/rsync Amazon Shuts Down Internal AI Leaderboard After Employees Cheated Open source project contains hidden instruction for “AI” agents: delete my code DOD wants to integrate cyber in all operations, and integrate security into AI Trump plan to test AI models has a problem—US security teams were gutted by DOGE Kevin Beaumont: “xAI have asked a court to stri…” - Cyberplace Commvault says it's time to rethink resiliency as AI crooks leave victims in a ‘dark, dead' state Attackers Use AI to Automate EDR Evasion Testing Pluralistic: Delusion as a service (04 Jun 2026) – Pluralistic: Daily links from Cory Doctorow These LLMs are the best at resisting Russian propaganda RAG Security and Privacy: Formalizing the Threat Model and Attack Surface From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability Will the Agent Recuse Itself? Measuring LLM-Agent Compliance with In-Band Access-Deny Signals Critical Hugging Face Transformers Vulnerability Enables Remote Code Execution Attacks La guerre, la guerre, c'est pas une raison pour se faire mal! Iran-Linked Hackers Destroy IT, Backups, and Recovery Systems in Cyberattack targeting Middle East Pentagon raised threat of Israeli spying on U.S. to highest level, sources say Souveraineté ou vive le numérique libre! EU plots long game against US digital supremacy OSI welcomes the European Union's “Tech Sovereignty” package Cable lobby warns of chaos if FCC doesn't relax ban on foreign routers Privacy ou cachez ces informations que je ne saurais voir The Pentagon Finally Admits That Location Data Is a Battlefield Problem Age verification for social media – the beginning of the end for a free internet? Privacy isn't dead: it's just that tech companies have made it inconvenient Amazon-owned Ring should pay Americans for scanning their faces, lawsuit says Elon Musk tries again to escape FTC audits of X data handling I am the law Policy-Compliant Cloud Storage Systems GrapheneOS user reported to authorities for using GrapheneOS Red ou tout ce qui est brisé Cachez ce fiasco que j'ai fait Microsoft's Zero-Day Legal Threats Spark Backlash Microsoft Clarifies It Won't Sue Security Researchers Amid Nightmare-Eclipse Controversy Microsoft reaches for olive branch after public dustup with 0-day researcher Nightmare Eclipse incident shows the researcher-vendor fights may never fully go away Another bug hunter leaks Microsoft exploits in defiance of company's handling of vulnerability disclosures Microsoft MSRC Allegedly Dismissed Dependency Confusion Vulnerability, Claims Researcher Just LOL BIN BAS Kevin Beaumont: “Wake up babe, new lolbins and …” - Cyberplace Microsoft's Coreutils project brings Linux commands to Windows Microsoft Investigates MFA Setup Failure and MySigns-In Portal Outage Dozens of Red Hat packages backdoored through its official NPM channel Inspector general finds NIST mistakes have made vulnerability database ineffective Sur le serveur X.Org, neuf nouvelles failles de sécurité dont huit débusquées par une IA HTTP/2 Bomb : une mini-requête suffit pour faire tomber nginx, Apache ou IIS Blue ou tout ce qui améliore notre posture - An Analysis of GrapheneOS's Server Infrastructure - Android phones will soon be able to detect spoofed calls and impersonation scams - Kernel-Level Ground Truth: Why eBPF is Replacing User-Space Agents for Security Observability - Dashlane explains how attackers managed to download encrypted password vaults - Let's Encrypt Unveils Merkle Tree Certificates to Secure the Web Against Quantum Threats Divers ou parce que j'ai aucune idée où les placer - The Infosec Phrasebook - United Airlines Flight To Spain Pulls U-Turn Over Bluetooth Device Name - Cyber Insurance Rates Are Dropping, but Exclusions Widen - DNS is for people - not for IT infrastructure - The US Military Quietly Turned GPS Into a Global ‘Numbers Station,' Evidence Suggests - I led the 2014 U.S. CDC Ebola response. An action plan is needed now - Teen social media ban risks strengthening Big Tech dominance: Bluesky Collaborateurs Nicolas-Loïc Fortin Crédits Montage par Intrasecure inc Locaux réels par Intrasecure inc

How do all your devices connect and stay safe in the cloud? In this episode, Lois Houston and Nikita Abraham talk with OCI instructors about the basics of how networks work and the simple steps that help protect them. You'll learn how information gets from one place to another, why tools like switches, routers, and firewalls are important, and what goes into keeping access secure. The discussion also covers how organizations decide who can enter their systems and how they keep track of activity.   Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript:  00:00 Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive. 00:12 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:38 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! In the last episode, we spoke about local area networks and domain name systems. Today, we'll continue our conversation on the fundamentals of networking, covering a variety of important topics.  01:03 Lois: That's right, Niki. And before we close, we'll also touch on the basics of security. Joining us today are two OCI instructors from Oracle University: Sergio Castro and Orlando Gentil. So glad to have you both with us guys. Sergio, with so many users and devices connecting to the internet, how do we make sure everyone can get online? Can you break down what Network Address Translation, or NAT, does to help with this? Sergio: The world population is bigger than 4.3 billion people. That means that if we were to interconnect every single human into the internet, we will not have enough addresses. And not all of us are connected to the internet, but those of us who are, you know that we have more than one device at our disposal. We might have a computer, a laptop, mobile phones, you name it. And all of them need IP addresses. So that's why Network Address Translation exists because it translates your communication from a private IP to a public IP address. That's the main purpose: translate. 02:18 Nikita: Okay, so with NAT handling the IP translation, how do we ensure that the right data reaches the right device within a network? Or to put it differently, what directs external traffic to specific devices inside a network? Sergio: Port forwarding works in a reverse way to Network Address Translation. So, let's assume that this PC here, you want to turn it into a web server. So, people from the outside, customers from the outside of your local area network, will access your PC web server. Let's say that it's an online store. Now all of these devices are using the same public IP address. So how would the traffic be routed specifically to this PC and not to the camera or to the laptop, which is not a web server, or to your IP TV? So, this is where port forwarding comes into play. Basically, whenever it detects a request coming to port, it will route it and forward that request to your PC. It will allow anybody, any external device that wants to access this particular one, this particular web server, for the session to be established. So, it's a permission that you're allowing to this PC and only to this PC. The other devices will still be isolated from that list. That's what port forwarding is. 03:48 Lois: Sergio, let's talk about networking devices. What are some of the key ones, and what role do they play in connecting everything together? Sergio: There's plenty of devices for interconnectivity. These are devices that are different from the actual compute instances, virtual machines, cameras, and IPTV. These are for interconnecting networks. And they have several functionalities. 04:11 Nikita: Yeah, I often hear about a default gateway. Could you explain what that is and why it's essential for a network to function smoothly? Sergio: A gateway is basically where a web browser goes and asks a service from a web server. We have a gateway in the middle that will take us to that web server. So that's basically is the router. A gateway doesn't necessarily have to be a router. It depends on what device you're addressing at a particular configuration. So, a gateway is a connectivity device that connects two different networks. That's basically the functionality.  04:47 Lois: Ok. And when does one use a default gateway? Sergio: When you do not have a specific route that is targeting a specific router. You might have more than one router in your network, connecting to different other local area networks. You might have a route that will take you to local area network B. And then you might have another router that is connecting you to the internet. So, if you don't have a specific route that will take you to local area network B, then it's going to be utilizing the default gateway. It directs data packets to other networks when no specific route is known. In general terms, the default gateway, again, it doesn't have to be a router. It can be any devices. 05:34 Nikita: Could you give us a real-world example, maybe comparing a few of these devices in action, so we can see how they work together in a typical network? Sergio: For example, we have the hub. And the hub operates at the physical layer or layer 1. And then we have the switch. And the switch operates at layer 2. And we also have the router. And the router operates at layer 3. So, what's the big difference between these devices and the layers that they operate in? So, hubs work in the physical layer of the OSI model. And basically, it is for connecting multiple devices and making them act as a single network segment. Now, the switch operates at the data link layer and is basically a repeater, and is used for filtering content by reading the addresses of the source and destination. And these are the MAC addresses that I'm talking about. So, it reads where the packet is coming from and where is it going to at the local area network level. It connects multiple network segments. And each port is connected to a different segment. And the router is used for routing outside of your local area network, performs traffic directing functions on the internet. A data packet is typically forwarded from one router to another through different networks until it reaches its destination node. The switch connects multiple network segments. And each port of the switch is connected to a different segment. And the router performs traffic directing functions on the internet. It takes data from one router to another, and it works at the TCP/IP network layer or internet layer. 07:34 Lois: Sergio, what kind of devices help secure a network from external threats? Sergio: The network firewall is used as a security device that acts as a barrier between a trusted internal network and an untrusted external network, such as the internet. The network firewall is the first line of defense for traffic that passes in and out of your network. The firewall examines traffic to ensure that it meets the security requirements set by your organization, or allowing, or blocking traffic based on set criteria. And the main benefit is that it improves security for access management and network visibility. 08:23 Are you keen to stay ahead in today's fast-paced world? We've got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you're always in the know, we offer New Features courses that give you an insider's look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started.  08:48 Nikita: Welcome back! Sergio, how do networks manage who can and can't enter based on certain permissions and criteria? Sergio: The access control list is like the gatekeeper into your local area network. Think about the access control list as the visa on your passport, assuming that the country is your local area network. Now, when you have a passport, you might get a visa that allows you to go into a certain country. So the access control list is a list of rules that defines which users, groups, or systems have permissions to access specific resources on your networks.  It is a gatekeeper, that is going to specify who's allowed and who's denied. If you don't have a visa to go into a specific country, then you are denied. Similar here, if you are not part of the rule, if the service that you're trying to access is not part of the rules, then you cannot get in. 09:49 Lois: That's a great analogy, Sergio. Now, let's turn our attention to one of the core elements of network security: authentication and authorization. Orlando, can you explain why authentication and authorization are such crucial aspects of a secure cloud network? Orlando: Security is one of the most critical pillars in modern IT systems. Whether you are running a small web app or managing global infrastructure, every secure system starts by answering two key questions. Who are you, and what are you allowed to do? This is the essence of authentication and authorization. Authentication is the first step in access control. It's how a system verifies that you are who you claim to be. Think of it like showing your driver's license at a security checkpoint. The guard checks your photo and personal details to confirm your identity. In IT systems, the same process happens using one or more of these factors. It will ask you for something you know, like a password. It will ask you for something that you have, like a security token, or it will ask you for something that you are, like a fingerprint. An identity does not refer to just a person. It's any actor, human or not, that interacts with your systems. Users are straightforward, think employees logging into a dashboard. But services and machines are equally important. A backend API may need to read data from a database, or a virtual machine may need to download updates. Treating these non-human identities with the same rigor as human ones helps prevent unauthorized access and improves visibility and security. After confirming your identity, can the system move on to deciding what you're allowed to access? That's where authorization comes in. Once authentication confirms who you are, authorization determines what you are allowed to do. Sticking with the driver's license analogy, you've shown your license and proven your identity, but that doesn't mean that you can drive anything anywhere. Your license class might let you drive a car, not a motorcycle or a truck. It might be valid in your country, but not in others. Similarly, in IT systems, authorization defines what actions you can take and on which resources. This is usually controlled by policies and roles assigned to your identity. It ensures that users or services only get access to the things they are explicitly allowed to interact with. 12:47 Nikita: How can organizations ensure secure access across their systems, especially when managing multiple users and resources?  Orlando: Identity and Access Management governs who can do what in our systems. Individually, authentication verifies identity and authorization grants access. However, managing these processes at scale across countless users and resources becomes a complex challenge. That's where Identity and Access Management, or IAM, comes in. IAM is an overarching framework that centralizes and orchestrates both authentication and authorization, along with other critical functions, to ensure secure and efficient access to resources.  13:35 Lois: And what are the key components and methods that make up a robust IAM system? Orlando: User management, a core component of IAM, provides a centralized Identity Management system for all user accounts and their attributes, ensuring consistency across applications. Key functions include user provisioning and deprovisioning, automating account creation for new users, and timely removal upon departure or role changes. It also covers the full user account lifecycle management, including password policies and account recovery. Lastly, user management often involves directory services integration to unify user information. Access management is about defining access permissions, specifically what actions users can perform and which resources they can access. A common approach is role-based access control, or RBAC, where permissions are assigned to roles and users inherit those permissions by being assigned to roles. For more granular control, policy-based access control allows for rules based on specific attributes. Crucially, access management enforces the principle of least privilege, granting only the minimum necessary access, and supports segregation of duties to prevent conflicts of interest. For authentication, IAM systems support various methods. Single-factor authentication, relying on just one piece of evidence like a password, offers basic security. However, multi-factor authentication significantly boosts security by requiring two or more distinct verification types, such as a password, plus a one-time code. We also have biometric authentication, using unique physical traits and token-based authentication, common for API and web services. 15:46 Lois: Orlando, when it comes to security, it's not just about who can access what, but also about keeping track of it all. How does auditing and reporting maintain compliance? Orlando: Auditing and reporting are essential for security and compliance. This involves tracking user activities, logging all access attempts and permission changes. It's vital for meeting compliance and regulatory requirements, allowing you to generate reports for audits. Auditing also aids in security incident detection by identifying unusual activities and providing data for forensic analysis after an incident. Lastly, it offers performance and usage analytics to help optimize your IAM system.  16:35 Nikita: That was an incredibly informative conversation. Thank you, Sergio and Orlando, for sharing your expertise with us. If you'd like to dive deeper into these concepts, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Lois: I agree! This was such a great conversation! Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 16:58 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Hubertus Meyer-Burckhardt ist zu Gast im Podcast "Feel Hamburg" und spricht über sein Leben zwischen Fernsehen, Theater, Literatur und Talkshow. Der langjährige Moderator der NDR Talk Show erzählt von seiner besonderen Verbindung zu Hamburg, seiner Liebe zum Grindelviertel und warum er sich selbst bis heute eher als Reisenden denn als Sesshaften sieht.Im Gespräch mit Daniel Kaiser geht es um die Anfänge in Hamburg in den 70er-Jahren, um das Studium von Geschichte und Philosophie, die Arbeit am Thalia Theater und den Weg vom Regieassistenten zum erfolgreichen Filmproduzenten, Autor und Moderator. Meyer-Burckhardt erklärt, warum gute Gespräche vor allem aus kurzen Fragen bestehen und weshalb Vorbereitung die Grundlage jeder gelungenen Talkshow ist.Außerdem spricht er offen über prägende Erfahrungen seiner Kindheit, über seinen alkoholkranken Vater, die Bedeutung seiner Großmutter "Osi" und darüber, wie Optimismus trotz schwieriger Lebensphasen entstehen kann. Themen wie Resilienz, Fernweh, Heimatgefühl und die Kunst, glücklich zu sein, ziehen sich durch das gesamte Gespräch.Natürlich geht es auch um Hamburg: um die Elbe, den Elbtunnel, Lieblingsorte wie das Grindelviertel, das Abaton-Kino und die Bücherstube Stolterfoht. Meyer-Burckhardt verrät außerdem, warum er die hanseatische Zurückhaltung schätzt, was ihn an der Stadt manchmal stört und weshalb er gern einmal selbst eine Hamburger U-Bahn fahren würde.Eine persönliche, humorvolle und zugleich nachdenkliche Folge über Lebensfreude, Neugier und die Fähigkeit, immer wieder neu anzufangen.Hier geht es zu Meyer-Burckhardts Podcastempfehlung bei ARD Sounds: https://www.ardsounds.de/sendung/berlin-code-mit-linda-zervakis/urn:ard:show:7d6b2a6353d8a1a6/

Alguna vez entraste a un lugar y en segundos supiste que no eras bienvenido. Nadie lo dijo en voz alta. Pero lo sentiste en el silencio, en la mirada, en cómo nadie se movió para hacerte espacio.Esa sensación no es exageración ni inmadurez. Es una herida real. Y muchos la hemos cargado durante años sin saber bien qué hacer con ella.En este mensaje, Danilo Montero acompaña una de las preguntas que más personas llevan en silencio: ¿hay lugar para mí?En este video aprenderás:• Por qué la necesidad de pertenecer no es debilidad, sino algo que Dios puso en ti desde el principio• Qué hacer cuando para encajar en un grupo sientes que tienes que sacrificar quién eres• Cómo la historia de José revela que el rechazo puede doler sin tener que definirte• Por qué la opinión de un grupo no es la última palabra sobre tu identidad• Cómo perdonar a quienes no pudieron valorarte, sin permitir que sigan haciéndote dañoSi alguna vez apagaste algo de ti para pertenecer a algún lugar, este mensaje podría decirte algo que necesitas escuchar hoy.

Shone Anstey of LQWD Technologies joins Pierre Rochard to discuss why Bitcoin and the Lightning Network are the essential "trust protocols" for the coming AI revolution. Learn how the Lightning Network acts as a major computer science breakthrough, extending the internet's OSI stack to allow money to move at the speed of intelligence for a machine economy bigger than the human economy.Chapters00:10 Effectively unlimited scale01:00 Shone Anstey's background in 90s tech02:32 Scaling Lightning like the early internet04:52 Technical requirements of nodes and channels08:25 Extending the internet's plumbing (OSI Stack)10:57 Bitcoin as an open protocol for trust15:57 Why the Lightning Network needs volume19:33 Why AI agents prefer Bitcoin30:59 Moving money at the speed of intelligence36:45 Will the Federal Reserve use Lightning?48:11 The Nakamoto Effect and Metcalfe's Law

On the latest NFL Players: Second Acts podcast, Peanut and Roman are joined by Super Bowl champion and international NFL ambassador Osi Umenyiora Harper for a powerful, wide‑ranging conversation about survival, gratitude, and the global rise of the NFL. Osi opens up about the medical emergency that put him in a coma for five days, and the clarity he gained on the other side. The guys dive into Osi’s groundbreaking work with the NFL International Player Pathway (IPP) program, his mission to expand the game across Africa and Europe, and the ripple effect of the NFL’s international games. It’s a vulnerable, inspiring, and global‑minded episode with one of the most influential voices in football today. The NFL Players: Second Acts podcast is a production of the NFL in partnership with iHeart Media.See omnystudio.com/listener for privacy information.

On the latest NFL Players: Second Acts podcast, Peanut and Roman are joined by Super Bowl champion and international NFL ambassador Osi Umenyiora Harper for a powerful, wide‑ranging conversation about survival, gratitude, and the global rise of the NFL. Osi opens up about the medical emergency that put him in a coma for five days, and the clarity he gained on the other side. The guys dive into Osi’s groundbreaking work with the NFL International Player Pathway (IPP) program, his mission to expand the game across Africa and Europe, and the ripple effect of the NFL’s international games. It’s a vulnerable, inspiring, and global‑minded episode with one of the most influential voices in football today. The NFL Players: Second Acts podcast is a production of the NFL in partnership with iHeart Media.See omnystudio.com/listener for privacy information.

Cilvēks ir iemācījies sintezēt teju visu, bet aizvien mums nav aizvietotāja cilvēka asinīm. Asinis nav vienkārši šķidrums, kas rit mūsu ķermenī - tas ir teju vesels orgāns, kura sarežģītā uzbūve un bioķīmija ir kas tāds, ko cilvēks atdarināt laboratorijas apstākļos aizvien nespēj. Bet varbūt kādu dienu spēs? Organiskās sintēzes institūtā (OSI) top pētījumi par to, kā varētu aizvietot šo dārgo šķidrumu un pētnieki ir soli tuvāk atbildei. Vai varam cerēt, ka nākotnē nebūs nepieciešami donori un asinis varēs saražot laboratorijā? Raidījumā Zināmais nezināmajā skaidro OSI Farmaceitiskās farmakoloģijas laboratorijas vadošais pētnieks, Baltijas biomateriālu ekseleces centra Pretklīnisko biomateriālu izpētes grupas vadītājs Antons Sizovs, OSI Farmaceitiskās farmakoloģijas laboratorijas vadošā pētniece, Baltijas biomateriālu ekseleces centra Pretklīnisko biomateriālu izpētes grupaszinātniece Baiba Švalbe. Sazināmies ar Valsts Asinsdonoru centra vadītāju Egitu Poli. Lai arī notiek pētījumi, pilnvērtīgi aizvietot nesanāks. Asinīs ir četri komponenti - plazma, eritrocīti, imūnšūnas un trombocīti. "Kad runājam par mākslīgajām asinīm, vairāk domājam par eritrocītiem, par asins spēju pārnest skābekli," skaidro Antos Sizovs. "Ja ir lieli asins zudumi, galvenais ir nodrošināt, lai skābeklis nokļūtu līdz audiem, kas ir dziļi ķermenī." Gadījumos, kur ir lieli asins zudumi, varētu izmantot mākslīgās asinis. Notiek vairāki pētījumi, lai izstrādātu mākslīgās asinis, arī OSI pētījums ir ar to saistīts.   Jauni materiāli kaulu un locītavu defektu aizstājēju jomā Jau no pirms vairākiem tūkstošiem gadu cilvēki ir meklējuši materiālus, kā aizstāt fiziskos defektus. Pētnieki ir atraduši gan ziloņkaula acs protēzi, gan no ziloņkaula izgatavotu kājas lielā īkšķa protēzi. Tiek uzskatīts, ka šie atradumi nāk no trešā gadu tūkstoša pirms Kristus. Šodien, skatot medicīnas zinātni ortožu un protēžu jomā, ir notikusi pamatīga evolūcija, sākot no koka kājām, kas reiz bija teju neaizstājams pirātu kapteiņa atribūts, ir nostaigāts garš ceļš līdz kaulaudu implantiem un kustīgām protēzēm. Kopā ar Rīgas Tehniskās universitātes  Dabaszinātņu un tehnoloģiju fakultātes profesoru, Baltijas Biomateriālu ekselences centra projekta vadītāju un Latvijas Zinātņu akadēmijas akadēmiķi Jāni Loču skatām, kas šodien ir pieejams un kādus jaunus materiālus šobrīd pēta un izstrādā kaulu un locītavu defektu aizstājēju jomā. Uzzināsim arī, kāpēc titāna implanti ir labāki par nerūsējošo tēraudu un kā tiek uzlaboti esošie implantu materiāli. -- Ja iepriekšējie stāsti vairāk bija par sugām, kas atbilst nosaukumam "pavasara vēstnesis", šoreiz stāsts par vasaras vēstnesi – svīri. Svīre ir putns, kurš Latvijā ierodas no dienvidiem viens no pēdējiem un uzturas te vien dažus mēnešus. Stāsta Latvijas ornitoloģijas biedrības pārstāve Ance Priedniece.

On the latest NFL Players: Second Acts podcast, Peanut and Roman are joined by Super Bowl champion and international NFL ambassador Osi Umenyiora Harper for a powerful, wide‑ranging conversation about survival, gratitude, and the global rise of the NFL. Osi opens up about the medical emergency that put him in a coma for five days, and the clarity he gained on the other side. The guys dive into Osi’s groundbreaking work with the NFL International Player Pathway (IPP) program, his mission to expand the game across Africa and Europe, and the ripple effect of the NFL’s international games. It’s a vulnerable, inspiring, and global‑minded episode with one of the most influential voices in football today. The NFL Players: Second Acts podcast is a production of the NFL in partnership with iHeart Media.See omnystudio.com/listener for privacy information.

Send us Fan MailPeaches is back for the May 18 Daily Drop, and this one goes everywhere—from bayonet charges and Indo-Pacific deterrence… to carrier deployments, combat rescue upgrades, shady investigations, and why artificial intelligence still can't replace grit.The United States Army is bringing bayonet assaults back to Ranger School, the USS Gerald R. Ford returns from an 11-month combat deployment, the United States Marine Corps is rehearsing island seizures across the Philippines, and the United States Air Force is finally buying new combat rescue radios after real-world recoveries proved the old gear isn't enough.Then Peaches goes off-script—breaking down the Monica Witt manhunt, telling a brutally honest OSI story, reacting to a midair collision in Idaho, praising the United States Coast Guard for making admirals take PT tests first, and calling out the Pentagon's chances of ever passing a clean audit.Bottom line: technology matters… but purpose, leadership, and people willing to keep going still win. ⏱️ Timestamps:00:00 Purpose Over Motivation 01:00 Tasty Gains & San Diego OTS 02:00 Bayonets Return to Ranger School 03:30 Why Air Defense Suddenly Matters 04:45 Indo-Pacific Burden Sharing 05:30 4,000 Soldiers Not Going to Poland 06:30 Army's Smart Scope vs Drones 07:20 USS Gerald R. Ford Returns After 11 Months 09:15 Long Deployments & Family Reintegration 10:15 Navy's Future Carrier Delayed 11:00 Navy Recruiting Through Gaming 12:00 Marines Prepare to Seize Islands 14:00 Air Force Finally Buys New Rescue Radios 16:00 Why Combat Rescue Was an Afterthought 17:30 Monica Witt and the $200K Bounty 18:30 Peaches Goes Off on OSI 23:00 GAO Calls Out Air Force Readiness 24:00 Idaho Midair Collision 25:30 United States Space Force Wants Longer Tours 27:00 Coast Guard Makes Admirals PT First 29:00 Three Cocaine Boats in One Day 30:00 Pete Hegseth Reviews Pentagon Legal System 31:30 Why the Pentagon Will Never Pass Audit 33:00 Donald Trump vs Iran 34:00 Strait of Hormuz Is Heating Up 35:00 Xi Jinping Warns the U.S. 36:00 Russia's Massive Drone Barrage 37:00 Final Thoughts

In this episode of VISITORS, Kelly sits down with Jeff Nuccetelli, Air Force veteran, federal law enforcement officer, and congressional UAP witness, whose 20-year silence about what happened at Vandenberg finally broke in front of Congress. Jeff walks through the October 2003 Red Square incursion, the night a craft landed on the flight line and an OSI colonel showed up the next morning, and why witnesses stopped calling in lights after one of his best officers was quietly threatened with career destruction. He gets into what it cost him personally to testify, the flood of new witnesses that came forward after the September hearing, and why he's convinced no help is coming from the UAP community or Congress and what he and Dylan Borland and Matthew Brown are doing about it.  He also talks about growing up with the Presque Isle landing in his family, traveling to Brazil with James Fox, and why he thinks reality itself is the thing that's actually classified. Find Jeff: X: https://twitter.com/Ice_Alchemist11 Instagram: @the_jeff_nuccetelli_podcast Podcast: https://www.youtube.com/@TheJeffNuccetelliPodcast Support Jeff: https://gofund.me/53a2558ef Subscribe on Apple Podcasts:  https://podcasts.apple.com/us/podcast/frightday/id951360425 Stream on Spotify: https://open.spotify.com/show/14ioP0zfFczK8hKPsDUmko?si=cff351ada3944a9a Want more? Join the Frightday Society, at http://thefrightdaysociety.org As a Society Member, you'll have access to all Screamium content (Behind the Screams, It's Been a Weird Week, A Conversation With..., Toast to Toast PM with Wine Kelly, Cinema Autopsy, the Writers' Room, bonus episodes of Captain Kelly's Cryptids & Conspiracies, Byron's Serial Corner, and so much more! You'll also be part of our interactive community dedicated to the advancement of horror, hauntings, cryptids, conspiracies, aliens, and true crime. All things frightening. Keep our mini-fridges full of blood...I mean...not blood...normal things that people drink...by going to http://shop.frightday.com  Theme music by Yawns Produced by Byron McKoy Follow us in the shadows at the following places: @byronmckoy @kellyfrightday @frightday  This is an Audio Wool Original. Keywords: UFO, UAP, Vandenberg, military, whistleblower, government secrecy, extraterrestrial, disclosure, paranormal, UFO incidents

CME credits: 1.00 Valid until: 20-04-2027 Claim your CME credit at https://reachmd.com/programs/cme/beyond-iop-integrating-ocular-surface-resilience-into-glaucoma-management/49097/ Glaucoma care is shifting from a narrow focus on pressure targets to a broader, patient-centered approach that balances durable intraocular pressure (IOP) control with preservation of the ocular surface, comfort, and real-world adherence. But, daily practice often still defaults to drop-heavy regimens that can erode the very ocular surface on which long-term success depends. The chronic use of preserved topical medications can compromise the cornea and conjunctiva, perpetuating ocular surface disease (OSD) and ocular surface inflammation (OSI). These conditions not only degrade comfort and quality of life but also undermine adherence, accelerate treatment failure, and reduce the success rates of both medical and surgical interventions. Recent expert consensus underscores that every glaucoma patient should be screened for OSD/OSI, yet implementation remains inconsistent in daily practice. Contemporary perspectives and data support a shift toward ocular surface-sparing strategies, including preservative-free options, earlier laser strategies, and newer tear-restorative, anti-inflammatory, and neuromodulatory therapeutic options when OSD does occur.=

A Victoria Cross recipient who has always denied being a war criminal has been arrested over five alleged war crimes murders, including two he’s accused of committing himself. Today - the case against Ben Roberts-Smith, and how he will defend himself. Read more: Roberts-Smith faces life in prison if convicted War crimes prosecutors will face challenges convicting Ben Roberts-Smith Hanson: I won’t abandon BRSSee omnystudio.com/listener for privacy information.

Esta noche te compartimos una serie de afirmaciones que te ayudarán a relajarte, soltar los pensamientos del día y preparar tu mente y tu cuerpo para un descanso profundo y reparador.–A lo largo de estos 4 años de Durmiendo Podcast, hemos compartido episodios que les han ayudado muchísimo. Por eso, hoy traemos de vuelta las herramientas que más han resonado con ustedes y que les han acompañado a cerrar su día con calma

Guest Jack Skinner Panelist Richard Littauer Show Notes In this episode of Sustain, host Richard Littauer talks with Jack Skinner, PyCon AU organizer and freelance consultant/fractional CTO, to explore why regional conferences matter so much to the long-term health of open source communities. Their conversation looks at how events like PyCon AU do far more than host talks, they create local connections, nurture future leaders, support first-time speakers, and help sustain the broader Python ecosystem in ways that global conferences alone cannot. Drawing on Jack's experience as a conference organizer and community builder, the episode offers a behind-the-scenes look at the challenges of running volunteer-led events, from sponsorships and logistics to burnout, accessibility, and building a stronger pipeline of future organizers. Press download now to hear more! [00:01:49] Jack shares his background and how he got involved in Python and event organizing. [00:02:48] We hear about Jack's first PyCon AU experience. [00:04:14] Jack describes PyCon AU, who it serves, and how it's changed after COVID. [00:07:01] Why do regional conferences exist alongside PyCon US? [00:09:24] Jack talks about what makes Australia and New Zealand different as conference communities. [00:10:55] PyCon AU's attendance goals are discussed as Jack mentions his big goal is to bring attendance back to roughly 500-600 people, restoring pre-pandemic strength. [00:12:04] The discussion turns to conference structure: tracks, workshops, and sponsor interest, with Jack emphasizing sponsorship is not just about money. [00:14:54] Richard asks how organizers know whether conferences help people learn, connect, or build community. Jack explains how they're measuring community impact beyond “good vibes” and rebuilding local Python communities. [00:17:34] Jack explains PyCon AU is trying to build a future organizer pipeline by letting people observe how conference planning works and introduces his proposed program/project, “shadow team.” [00:19:09] Another project Jack is working on is documenting the behind-the-scenes work of organizing the conference through long-form writing. [00:20:38] Jack admits he feels imposter syndrome because he's not paid to write Python, his contribution is centered on the sociotechnical side. [00:23:20] PyCon AU's independence from government and institutions is discussed, and how the conference community is globally aware, even if locally focused. [00:27:05] Call for proposals details, deadline is March 29, and the in-person focus for this year's event are mentioned. Richard discusses the return of the academic track and Jack details more info on poster sessions and workshop submissions. [00:32:08] Volunteering and buying tickets are explained and why you should buy tickets early if you can. Quotes [00:32:20] “Volunteering is an awesome way to be involved in PyCon.” Spotlight [00:35:16] Richard's spotlight is two of his lecturers at the University of Edinburgh, Simon Kirby and Andrew Smith, who introduced him to Python. [00:35:55] Jack's spotlight is two companion projects: pretalx and pretix. Links SustainOSS podcast@sustainoss.org richard@sustainoss.org SustainOSS Discourse SustainOSS Mastodon SustainOSS Bluesky SustainOSS LinkedIn Open Collective-SustainOSS (Contribute) Richard Littauer Socials Jack Skinner LinkedIn Jack Skinner Website PyCon AU, August 26-30, 2026, Brisbane PyCon AU News & Updates Sustain Podcast-Episode 75: Deb Nicholson on the OSI, the future of open source, and SeaGL Sustain Podcast-Episode 137: A How-to Guide for Contributing to Open Source as an Employee, for Corporations (featuring Deb Nicholson as Host) Guido van Rossum Whale song shows language-like statistical structure Simon Kirby (co-lead author) pretalx (GitHub) pretix (GitHub) Sponsor CURIOSS Credits Produced by Richard Littauer Edited by Paul M. Bahr at Peachtree Sound Show notes by DeAnn Bahr Peachtree Sound Special Guest: Jack Skinner.

W drugim odcinku cyklu „Czytamy Ukrainę” rozmawiamy z Aleksandrą Brzuzy, tłumaczką książki Łesia Bełeja „Plan naprawy Ukrainy” (wyd. Ha!art, 2023).Osią rozmowy jest esej ukraińskiego pisarza i publicysty, który wskazuje na pakiet problemów współczesnej Ukrainy – od wyzwań instytucjonalnych i społecznych po pytania o przyszłość państwa funkcjonującego w warunkach wojny.

Please enjoy this encore of Word Notes. A layer seven firewall designed to block threats at the application layer of the open system interconnection model, the OSI model.  CyberWire Glossary link: ⁠https://thecyberwire.com/glossary/web-application-firewall⁠ Audio reference link: “VCF East 9.1 - Ches' Computer Security Adventures - Bill Cheswick.” YouTube, 29 Dec. 2015, ⁠https://youtu.be/trR1cuBtcPs⁠. 

Please enjoy this encore of Word Notes. A layer seven firewall designed to block threats at the application layer of the open system interconnection model, the OSI model.  CyberWire Glossary link: ⁠https://thecyberwire.com/glossary/web-application-firewall⁠ Audio reference link: “VCF East 9.1 - Ches' Computer Security Adventures - Bill Cheswick.” YouTube, 29 Dec. 2015, ⁠https://youtu.be/trR1cuBtcPs⁠.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Hola mi gente! Today we are going to read, translate and listen The Song: Tití Me Preguntó by Bad Bunny. I will be reading the song in Spanish very slowly and you will try to understand word by word. You will be learning some interesting words and new vocabulary and also you will be improving your listening skills in Spanish. I will translate the song in English and then read in Spanish again in a normal speed but explaining some words at the same time.. You can support me and my podcast if you want:Donate with PayPal:https://www.paypal.com/paypalme/spanishwithdennisYou can buy me a cup of coffee here:https://www.buymeacoffee.com/spanishwithdennisHere are the lyrics:Ey, Tití me preguntó si tengo muchas novia'Muchas novia'Hoy tengo a una, mañana otraEy, pero no hay bodaTití me preguntó si tengo muchas novia', jeMuchas novia'Hoy tengo una, mañana otraMe la' voy a llevar a to'a pa' un VIPUn VIP, eySaluden a titíVamo' a tirarno' un selfie, say cheese, eyQue sonrían las que ya les metíEn un VIP, un VIP, eySaluden a titíVamo' a tirarno' un selfie, say cheeseQue sonrían las que ya se olvidaron de míMe gustan mucho las GabrielaLas Patricia, las Nicole, las SofíaMi primera novia en kinder, MaríaY mi primer amor se llamaba ThalíaTengo una colombiana que mе escribe to' los día'Y una mexicana que ni yo sabíaOtra en San Antonio que me quiere todavíaY las de PR que todita' son mía'Una dominicana que es uva bombónUva, uva bombónLa de Barcelona que vino en aviónY dice que mi bicho está cabrónYo dejo que jueguen con mi corazónQuisiera mudarme con todas pa' una mansiónEl día que me case, te envío la invitaciónMuchacho, deja eso, eyTití me preguntó si tengo muchas novia'Muchas novia'Hoy tengo una, mañana otraEy, pero no hay bodaTití me preguntó si tengo muchas novia'Ey, ey, muchas novia'Hoy tengo una, mañana otraTití me preguntó-tó-tó-tó-tó-tó-tó-tóTití me preguntó-tó-tó-tó-tó-tó-tó-tó (qué pámpara)Tití me preguntó-tó-tó-tó-tó-tó-tó-tóTití me preguntó-tó-tó-tó-tó(Pero ven acá, muchacho)(¿Y para qué tú quiere' tanta' novia'?)Me la' voy a llevar a to'a pa' un VIPUn VIP, eySaluden a TitíVamo' a tirarno' un selfie, say cheese, eyQue sonrían las que ya les metíEn un VIP, un VIP, eySaluden a TitíVamo' a tirarno' un selfie, say cheeseQue sonrían las que ya se olvidaron de mí(Oye, muchacho 'el diablo azaroso)(Suelta ese mal vivir que tú tiene' en la calle)(Búscate una mujer seria pa' ti)(Muchacho 'el diablo)(Coño)Yo quisiera enamorarmePero no puedoPero no puedo, eh, ehYo quisiera enamorarmePero no puedoPero no puedoSorry, yo no confío, yo no confíoNah, ni en mí mismo confíoSi quieres quedarte hoy que hace fríoY mañana te va', nahMuchas quieren mi baby gravyQuieren tener mi primogénito, eyY llevarse el créditoYa me aburrí, hoy quiero un totito inédito, jeUno nuevo, uno nuevo, uno nuevo, uno nuevoHazle caso a tu amigaElla tiene razónYo voy a romperte el corazónVoy a romperte el corazónEy, no te enamores de míNo te enamores de mí, eySorry, yo soy así, eyNo sé por qué soy asíHazle caso a tu amigaElla tiene razónYo voy a romperte el corazónVoy a romperte el corazónNo te enamores de mí (no)No te enamores de mí (no), noSorry, yo soy asíYa no quiero ser así, noThe Link of The Song:https://www.youtube.com/watch?v=qBUKfQRbzuk&pp=ygUQdGl0aSBtZSBwcmVndW50bw%3D%3DMy new Youtube channel: Spanish with Dennishttps://www.youtube.com/channel/UCQVuRUMQGwtzBIp1YAImQFQMy new Discord server and chat and you can already join and write to me there:https://discord.gg/HWGrnmTmyCMy new Telegram channel and you can already join and write to me or comment there:https://t.me/SpanishwithDennisJoin my Patreon:https://www.patreon.com/spanishwithdennisSupport me by joining my podcasts supporter club on Spreaker:https://www.spreaker.com/podcast/slow-spanish-language--5613080/supportDonate with Boosty:https://boosty.to/spanishwithdennis/donateDonate with Donation Alerts:https://www.donationalerts.com/r/dennisespinosaDonate with Crypto currency:Bitcoin (BTC)1DioiGPAQ6yYbEgcxEFRxWm5hZJcfLG9V6USDT (ERC20)0xeb8f678c0b8d37b639579662bf653be762e60855USDT (TRC20)TXoQwsaiTGBpWVkyeigApLT8xC82rQwRCNEthereum (ETH)0xeb8f678c0b8d37b639579662bf653be762e60855If you have any other suggestions or recommendations on what other platform you can support me and my podcasts, please let me know. You can write to me on telegram.Thanks in advance!! Gracias por adelantado!My other podcasts you can find it on different platforms and apps:1-  Comprehensible Spanish Language Podcast2 - Crazy Stories in Spanish Podcast3 - TPRS Spanish Stories

Take a Network Break! We start with a trio of follow-ups, including a correction regarding Mplify certifications, Cisco proposing new OSI layers, and free-space optics. Our Red Alert sounds off about a remote code execution vulnerability in the Ivanti Endpoint Manager Mobile agent. On the news front, Broadcom announces new silicon for wireless APs for... Read more »

Take a Network Break! We start with a trio of follow-ups, including a correction regarding Mplify certifications, Cisco proposing new OSI layers, and free-space optics. Our Red Alert sounds off about a remote code execution vulnerability in the Ivanti Endpoint Manager Mobile agent. On the news front, Broadcom announces new silicon for wireless APs for... Read more »

Take a Network Break! We start with a trio of follow-ups, including a correction regarding Mplify certifications, Cisco proposing new OSI layers, and free-space optics. Our Red Alert sounds off about a remote code execution vulnerability in the Ivanti Endpoint Manager Mobile agent. On the news front, Broadcom announces new silicon for wireless APs for... Read more »

Épisode 1431 : Si vous n'avez pas fait la dernière mise à jour de Spotify vous allez avoir une forte envie de la télécharger pour découvrir un nouvel univers à la fois audio et vidéo.On avait vu apparaître petit à petit la vidéo dans l'expérience de l'application avec d'un côté certains podcasts qui se voyaient affichés en vidéo pour ceux qui avaient uploadé le format.Et de l'autre des vidéos Lifestyle basées sur tous centres d'intérêts qui étaient sourcées et intégrées à la plateforme.Une plateforme qui bascule vers la vidéoSpotify est en train de se « YouTube-iser ». La vidéo arrive partout dans l'app. Ce n'est pas un petit test en douce, c'est une vraie stratégie produit.Spotify veut se battre frontalement avec YouTube, TikTok et YouTube Music et propose désormais des clips, des podcasts vidéo et des espaces dédiés aux créateurs. —Les clips peuvent maintenant remplacer la simple pochette avec un bouton qui te fait basculer en un tap de l'audio à la vidéo, ce qui casse le réflexe habituel « j'écoute sur Spotify / je vais voir le clip sur YouTube ». Le feed d'accueil a été redesigné façon TikTok ou Reels et un intègre désormais un scroll vertical d'aperçus vidéo et audio (musique, podcasts, livres audio), et ça change complètement la manière dont tu découvres du contenu. En parallèle, Spotify investit fort sur les podcasts vidéo et sur un programme de monétisation pour les créateurs, avec des revenus directement liés à la consommation vidéo des abonnés Premium.Ce que Spotify est en train de faire sur la vidéoL'app glisse doucement d'un lecteur audio à une plateforme où l'image pèse autant que le son. Vous voulez des exemples :-L'interface s'adapte petit à petit pour pousser aussi des formats courts, des extraits vidéos qui accrochent vite et qui donnent envie de rester dans l'app. Le fait d'intégrer les clips vidéo permet de retenir l'utilisateur là où, avant, il serait parti sur YouTube pour voir le clip. —Une mutation est en place depuis 5 ans maintenantMontée en puissance des podcasts vidéoSi on remonte un peu, dès 2020 Spotify commence à tester les podcasts vidéo sur un nombre très limité d'émissions.Ensuite, ça s'ouvre progressivement en 2021, notamment via Spotify for Podcasters. Aujourd'hui, Spotify héberge près de 500 000 podcasts vidéo.Sur la partie vidéo musicale pure, Spotify commence à tester les clips début 2025 dans une centaine de pays, en travaillant avec les majors et des labels indés.—Pourquoi Spotify veut “croquer” YouTubeUne bataille pour le temps passé et la pubDerrière tout ça, le move est assez clair : Spotify veut augmenter le temps passé dans l'app et la valeur pub.Il faut savoir que la pub en vidéo, se monétise beaucoup mieux que l'audio pur. Aujourd'hui, YouTube rafle une énorme partie de la valeur autour de la musique et des podcasts, parce que c'est là qu'on trouve les clips, les interviews filmées, les talk-shows, bref, tous les formats vidéo autour de l'audio. Spotify cherche donc à rapatrier cette attention et ces budgets chez lui. En parallèle, la plateforme veut s'installer comme une vraie alternative pour les créateurs vidéo et les podcasteurs, avec des modèles de partage de revenus et une visibilité qui se rapproche le plus possible de ce qu'ils peuvent obtenir sur YouTube.…Retrouvez toutes les notes de l'épisode sur www.lesuperdaily.com ! Le Super Daily est le podcast quotidien sur les réseaux sociaux. Il est fabriqué avec une pluie d'amour par les équipes de Supernatifs. Nous sommes une agence social media basée à Lyon : https://supernatifs.com. Ensemble, nous aidons les entreprises à créer des relations durables et rentables avec leurs audiences. Ensemble, nous inventons, produisons et diffusons des contenus qui engagent vos collaborateurs, vos prospects et vos consommateurs. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Send us a textPeaches dives headfirst into the swamp of veteran drama—Tim Kennedy, Shrek McPhee, stolen valor call-outs, and the internet's obsession with dragging up skeletons. He calls BS on the witch hunts, breaks down how accusations wreck careers long before proof, and exposes the military justice system's shady double standards. From OSI horror stories to generals cashing in on their rank, nothing's off-limits. If you think this episode is about playing nice—you're already lost.⏱️ Timestamps: 0:00 – Peaches sets the stage: busy week, no fluff 1:10 – Nashville and Vegas OTS updates 2:30 – Tim Kennedy, Shrek, and stolen valor heat 5:00 – Why dragging old dirt ruins everyone 7:00 – OSI investigations and dirty tactics 10:00 – Sexual assault accusations gone sideways 13:00 – Wrong name, wrong career destroyed 15:00 – Drawing the line: stolen valor vs personal lives 16:00 – Goggins and the deadbeat dad smear 17:00 – Corrupt generals cashing in post-retirement 18:30 – Peaches signs off (for now)

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

I was truly inspired by today's guest and her courage to follow her inner guidance, which served her not only on her personal journey but also led her to found a beautiful company with a sacred mission. Osi Mizrahi is the founder and visionary behind OSI Oils—a wellness brand rooted in slow beauty, Ayurvedic ritual, and the art of coming home to oneself. Born on a farm in Israel, Osi's connection to the natural world began early, shaping her journey as a medical intuitive, Ayurvedic artisan, and guide in the healing arts. With over 30 years of experience in yoga, Ayurveda, Reiki, Kundalini, and Theta Healing, Osi blends ancient wisdom with feminine embodiment. Her work is inspired by Kabbalah, breathwork, and intuitive practices that support deep transformation. Osi believes in slow beauty and slow aging—practices that honor the body's rhythms and nourish vitality over time. Oils, for her, are more than skincare—they are vessels of self-love, sensuality, and spiritual healing. Each OSI Oils formula is handcrafted in small batches using traditional Ayurvedic methods, turning everyday routines into sacred rituals. Whether soothing the nervous system with her belly oil or igniting life-force energy with Radiance Hair Oil, her products are designed to restore from the inside out. Osi's upcoming book shares her own journey through heartbreak, healing, and the power of ancient rituals. Through her storytelling, mentorship, and botanical creations, she helps women around the world reconnect to their spark—and live with more pleasure, purpose, and presence. Connect with Osi via: Email: osi@osioils.com Website: Osi Oils IG: @osioils YT: @OsiMizrahi Linked In: Osi Mizrahi Use My Special Promo Code RAWFORK20 for 20% off orders on osioils.com Visit https://marinabuksov.com for more holistic content. Music from https://www.purple-planet.com. Disclaimer: Statements herein have not been evaluated by the Food and Drug Administration. Products listed are not intended to diagnose, treat, cure, or prevent any diseases.

Send us a textPeaches flies solo and unfiltered, taking you on a no-holds-barred ride through shady OSI tactics, the SIG M18 controversy, and why the Air Force might just toss a junior enlisted under the bus to protect billion-dollar contracts. He drags lazy PT culture through the mud, skewers the “extra 800 meters will kill us all” crowd, and asks the real question—are new policies actually helping prevent suicides, or is it just more PowerPoint theater? From dark humor to brutal honesty, this is Peaches in full “crusty retired PJ” mode—raw, opinionated, and asking you for answers.

I was truly inspired by today's guest and her courage to follow her inner guidance, which served her not only on her personal journey but also led her to found a beautiful company with a sacred mission. Osi Mizrahi is the founder and visionary behind OSI Oils—a wellness brand rooted in slow beauty, Ayurvedic ritual, and the art of coming home to oneself. Born on a farm in Israel, Osi's connection to the natural world began early, shaping her journey as a medical intuitive, Ayurvedic artisan, and guide in the healing arts. With over 30 years of experience in yoga, Ayurveda, Reiki, Kundalini, and Theta Healing, Osi blends ancient wisdom with feminine embodiment. Her work is inspired by Kabbalah, breathwork, and intuitive practices that support deep transformation. Osi believes in slow beauty and slow aging—practices that honor the body's rhythms and nourish vitality over time. Oils, for her, are more than skincare—they are vessels of self-love, sensuality, and spiritual healing. Each OSI Oils formula is handcrafted in small batches using traditional Ayurvedic methods, turning everyday routines into sacred rituals. Whether soothing the nervous system with her belly oil or igniting life-force energy with Radiance Hair Oil, her products are designed to restore from the inside out. Osi's upcoming book shares her own journey through heartbreak, healing, and the power of ancient rituals. Through her storytelling, mentorship, and botanical creations, she helps women around the world reconnect to their spark—and live with more pleasure, purpose, and presence. Connect with Osi via: Email: osi@osioils.com Website: Osi Oils IG: @osioils YT: @OsiMizrahi Linked In: Osi Mizrahi Use My Special Promo Code RAWFORK20 for 20% off orders on osioils.com Visit https://marinabuksov.com for more holistic content. Music from https://www.purple-planet.com.

Guests Rynn Mancuso | Maryblessing Okolie | Mo McElaney Panelist Richard Littauer | Eriol Fox Show Notes In this episode of Sustain, Richard and Eriol talk with members of the Organization for Ethical Source (OES), Rynn Mancuso, Maryblessing Okolie, and Mo McElaney, about how ethics, licensing, and codes of conduct intersect in open source. They unpack the origins and challenges of the Hippocratic License, the community driven overhaul of Contributor Covenant 3.0, what it really takes to collaborate across borders and cultures, and how OES is now turning its attention to ethical AI, translations and practical resources for communities to make it a safer and more inclusive space. They also suggest ways for listeners to get involved in these important initiatives. Hit download now! [00:02:17] Rynn gives the elevator pitch on what the Organization for Ethical (OES) is. [00:04:57] Mo explains the Hippocratic License is modeled on “do no harm” and it's an open source license. [00:06:06] Richard wonders if the Hippocratic License is open source since we're not using OSI's definition. Mo explains that OES still uses “open source” in a broader, “big tent” sense focused on work done in the open, and Rynn adds why definitions need to evolve. [00:09:27] Rynn shares rewriting the Contributor Covenant 3.0, starting from their background, to being a limited scope, and getting feedback from translators that language was too American/Western and 3.0 needed a broader cultural fit. [00:15:12] Maryblessing was brought in to lead v3.0 from an African, non-US perspective and to make the process community driven. She tells us what's new in the Contributor Covenant 3.0. [00:19:43] The discussion covers how they all worked together. It was a highly collaborative, consensus driven process where anyone could propose edits. They talk about how long it took, not work entirely on GitHub, and why not everything was public. [00:24:59] We hear about some adoption challenges for codes of conduct for small projects and enterprises. [00:28:53] Rynn, Mo, and Maryblessing touch on how they are approaching ethical AI work, they share options to support OES, how to get involved, and translation needs. Quotes [00:12:32] “It was a very limited scope, and we always designed it to work on the internet and be for open source projects.” [00:13:23] “I would get these problems that really had to do with caste, but nobody would say anything about caste.” [00:16:37] “This new version also emphasizes restorative justice, and we're keen on using inclusive languages.” [00:17:06] “We're making progress on bringing in African translation.” [00:17:38] “One of the things we did with the new website was to include the CC3 builder which was going to help make it easy for people to adapt the code of conduct.” [00:21:37] “Every bit of feedback we got, we took it seriously, we talked about it.” [00:22:13] “It took is a year and six months to do the entire thing, to make sure people were available. It took that long because we wanted to make sure we were incorporating every feedback.” [00:23:14] “We do not do everything in the open on GitHub. One reason is structural. GitHub is not great at document management. Another reason we do that is we've received a lot of harassment form groups on the internet that were frankly invested in being able to cause trouble for a lot of people.” [00:29:14] “We're in the early stages of considering how we could approach ethical AI.” Spotlight [00:33:12] Mo's spotlight is for more folks to get involved with this project and other projects through the OES. [00:33:34] Rynn's spotlight is a shoutout to the folks at IBM and RedHat and Dev/Mission and JVS where they volunteer. [00:35:25] Maryblessing's spotlight is all the amazing people that helped put together the Contributor Covenant v.3.: Greg Cassel, Coraline Ada Ehmke, Gerardo Lisboa, Rynn Mancuso, Mo McElaney, Maryblessing Okolie, Ben Sternthal, and Casey Watts. [00:36:11] Eriol's spotlight is the OpenSSF Working Group on Securing Software Repositories. [00:36:44] Richard's spotlight is a fun paper called, Paradoxes of Openness: Trans Experiences in Open Source Software by Hana Frluckaj, Nikki Stevens, James Howison, and Laura Dabbish. Links SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) richard@sustainoss.org (mailto:richard@sustainoss.org) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) SustainOSS Bluesky (https://bsky.app/profile/sustainoss.bsky.social) SustainOSS LinkedIn (https://www.linkedin.com/company/sustainoss/) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Eriol Fox X (https://x.com/EriolDoesDesign) Rynn Mancuso LinkedIn (https://www.linkedin.com/in/rynnmancuso/) Maryblessing Okolie LinkedIn (https://www.linkedin.com/in/maryblessingokolie/?originalSubdomain=ng) Mo McElaney LinkedIn (https://www.linkedin.com/in/maureenmcelaney/) Organization For Ethical Source (OES) (https://ethicalsource.dev/) OES- What We Do (https://ethicalsource.dev/what-we-do/) OES-What We Believe (https://ethicalsource.dev/what-we-believe/) Donate-The Organization for Ethical Source (Open Collective) (https://opencollective.com/ethical-source) Contributor Covenant (https://www.contributor-covenant.org/) Contributor Covenant 3.0 Code of Conduct (https://www.contributor-covenant.org/version/3/0/code_of_conduct/) Code of conduct enforcement guidelines (MDN Web Docs) (https://developer.mozilla.org/en-US/docs/MDN/Community/Community_Participation_Guidelines) Coraline Ada Ehmke (https://en.wikipedia.org/wiki/Coraline_Ada_Ehmke) Ethical Source- Beacon (https://github.com/EthicalSource/beacon) Adopt Contributor Covenant (https://www.contributor-covenant.org/adopt/) Resources for Community Moderators (https://www.contributor-covenant.org/resources/) Dev/Mission (https://devmission.org/) JVS (Jewish Vocational Services) (https://jvs.org/) Techtonica (https://techtonica.org/) OpenSSF Working Group on Securing Software Repositories (https://github.com/ossf/wg-securing-software-repos) Paradoxes of Openness: Trans Experiences in Open Source Software (ACM Digital Library) (https://dl.acm.org/doi/abs/10.1145/3687047) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Maryblessing Okolie, Maureen Mcelaney, and Rynn Mancuso.

Snowflake VP of Product Management Chris Child joins Tristan Handy to unpack Snowflake's AI roadmap and what it means for data teams. They discuss the evolution from Snowpark to Cortex and Snowflake Intelligence, how to govern agents with row- and column-level controls, and why Snowflake is investing in Apache Iceberg and the Open Semantic Interchange initiative (dbt Labs recently open sourced MetricsFlow, the technology that powers the dbt Semantic Layer, to align with the goals of OSI). Chris also shares a vision for the next five years of data engineering: fewer bespoke pipelines, more standardization and semantics, and a bigger focus on business context and data products. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Guests Dawn Wages | Loren Crary Panelist Richard Littauer Show Notes In this episode of Sustain, Richard Littauer talks with Dawn Wages, former Chair of the Python Software Foundation board and Loren Crary, Deputy Executive Director of the PSF, about how the PSF sustains Python and its community, governance, fundraising, and events like PyCon US, and why they ultimately turned down a $1.5M NSF grant rather than accept new anti-DEI conditions. They walk through what the grant was for, how the decision unfolded, the financial and ethical risks involved, and the overwhelming community response in donations and support, ending with a call to participate in the PSF fundraiser and submit talks to PyCon US 2026. Press download now to hear more! [00:02:41] Dawn explains she just finished her term as Chair at the PSF Board, previously served as Treasurer, and that board seats are elected volunteer toles with three-year terms. [00:03:40] Loren describes her job as Deputy Executive Director, #2 to ED Deb Nicholson. She leads fundraising and revenue strategy, handles internal operations and strategic planning, and she clarifies that the Python Steering Council steers the language itself and mentions PyCon US will be in Long Beach, CA May 2026. [00:05:38] Dawn shares a personal story how PSF funding and local Python user group helped her start in Python a decade ago and encourages listeners to donate and use company matching. [00:06:57] Loren speaks about sponsors and individual donors and plugs the fundraiser and the “cute snake thermometer” on the donate page. [00:08:00] Richard, as a board member of Python New Zealand, underscores PSF's support for Python user groups and conferences. He then pivots to ask about strategy where Loren describes how the board leads strategy. [00:13:34] Dawn reflects on learning to chair the board for the first time, praising staff expertise, and she describes the ‘flywheel' model where staff and board collaborate closely, with staff often joining board meetings to co-develop strategy. [00:15:18] Loren highlights the PSF board and representation. [00:16:59] Richard gives a special shout-out to Phyllis Dobbs as one of the “unsung heroes” of open source, noting her work with OSI and Deb in the past. [00:17:26] The convo turns to the NSF Safe OSE program and what happened with the large grant the PSF was awarded and then declined. Loren details everything that happened and gives a shout-out to Seth Larson, whom she collaborated with. [00:29:00] Loren reads the key clause that PSF would need to affirm, and the board ultimately made the call that it was too risky to their mission to accept the terms. [00:31:42] Dawn explains the board's decision to withdraw and Loren notes that no one on the board or staff ever floated “dropping DEI to take the money.” [00:33:55] Dawn points to Python's reputation as a welcoming, diverse community and DEI is portrayed as “lifeblood,” not an optional extra. [00:35:03] What happened after they said they weren't taking the money? Dawn and Loren recount an outpouring of support after the public statement, and we find out how much money the fundraiser has made so far along including an anonymous donation. [00:38:33] Dawn zooms out to decades of conversations about funding open source, arguing that individual donors and major AI companies profiting from Python should be contributing at scale. [00:41:20] Richard reinforces the ongoing donation, and Loren plugs the PyCon US Call for Proposals (open through December 19) with new AI and security tracks and invites listeners to submit. Quotes [00:07:09] “If you want to know what a nonprofit does, look at who their funders are and that's who they're working for.” [00:12:07] “The board sets a strategy, but there needs to be a ‘flywheel' from the staff to keep things like that going.” [00:18:45] “We dipped our toes into grant funding, and we thought that would be a great way to make our work more sustainable.” [00:32:40] “The $1.5 million is not net worth putting the future health and safety of the language in the organization in jeopardy.” [00:32:58] “I am proud that at no point did anyone float: What if we just stopped doing everything DEI and take the money?” [00:38:09] “I like my boss to be the users.” [00:38:41] “We've been talking about what it means to fund open source for decades…I think this is an interesting arc that we're experiencing. I'm hoping that the numbers will have two or three commas from individual donations.” Spotlight [00:42:15] Richard's spotlight is Phyllis Dobbs. [00:42:26] Dawn's spotlight is PyScript. [00:42:42] Loren's spotlight is The Carpentries. Links SustainOSS (https://sustainoss.org/) podcast@sustainoss.org (mailto:podcast@sustainoss.org) richard@sustainoss.org (mailto:richard@sustainoss.org) SustainOSS Discourse (https://discourse.sustainoss.org/) SustainOSS Mastodon (https://mastodon.social/tags/sustainoss) SustainOSS Bluesky (https://bsky.app/profile/sustainoss.bsky.social) SustainOSS LinkedIn (https://www.linkedin.com/company/sustainoss/) Open Collective-SustainOSS (Contribute) (https://opencollective.com/sustainoss) Richard Littauer Socials (https://www.burntfen.com/2023-05-30/socials) Dawn Wages Website (https://dawnwages.info/) Loren Crary LinkedIn (https://www.linkedin.com/in/loren-crary/) Python Software Foundation (http://www.python.org/psf/) PSF Donate (https://donate.python.org/) PyCon US 2026, Long Beach, CA (https://us.pycon.org/2026/) The Philadelphia Python Users Group (PhillyPUG) (https://www.meetup.com/phillypug/) Safety, Security, and Privacy of Open Source Ecosystems (Safe-OSE) (https://www.nsf.gov/funding/opportunities/safe-ose-safety-security-privacy-open-source-ecosystems) PSF Welcomes New Security Developer in Residence with Support from Alpha-Omega (https://openssf.org/blog/2023/06/22/psf-welcomes-new-security-developer-in-residence-with-support-from-alpha-omega/) Seth Michael Larson-GitHub (https://github.com/sethmlarson) Seth Larson Blog post: I am the first PSF Security Developer-in-Residence (https://sethmlarson.dev/security-developer-in-residence) Python Software Foundation turns down $1.5 million NSF grant because of the anti-DEI strings attached (The Verge) (https://www.theverge.com/news/808268/python-software-foundation-turns-down-1-5-million-nsf-grant-because-of-the-anti-dei-strings-attached) The PSF has withdrawn a $1.5 million proposal to US government grant program (PSF Blog post) (https://pyfound.blogspot.com/2025/10/NSF-funding-statement.html) PSF Board Meeting Minutes Archive (Python) (https://www.python.org/psf/records/board/minutes/) Phyllis Dobbs (https://www.linkedin.com/in/phyllisadobbs/) PyScript (https://pyscript.net/) The Carpentries (https://carpentries.org/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Dawn Wages and Loren Crary.

Steven Dickens maintains his long-term bullish view on Oracle (ORCL). While he notes the company's ballooning debt, he sees Oracle's business growth staying intact through core OSI and cloud backlog. Customers from Alphabet (GOOGL), Amazon (AMZN), and OpenAI add to Steven's belief that Oracle has plenty of room to grow. Tom White offers an example options trade for the stock. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

In Part Two, the investigation into the Andersen Air Force Base ambush closes in on its prime suspect: SrA Jose Simoy. As Simoy goes on the run, FBI, OSI, and Security Police search the island of Guam for a killer hiding behind wigs, aliases, and threats. What follows is a dramatic capture, a capital court-martial, and a landmark death sentence (the first on Guam in 44 years). But a death sentence doesn't always mean death… Margot also follows the journeys of the co-conspirators, the emotional and physical aftermath for the two survivors, and the legacy of Sergeant Stacy Levay, a newlywed defender whose life was taken far too soon. This is the conclusion to one of the most brazen crimes ever committed on an Air Force installation.  ⸻

Ram Ramanathan, Vice President of Product at Ribbon Communications, joined Doug Green, Publisher of Technology Reseller News, to discuss Acumen, Ribbon's new AI-powered platform designed to accelerate autonomous networking for service providers and enterprises. Ramanathan explains that rapid shifts—5G adoption, cloud-native architectures, heightened security demands, and a retiring telecom workforce—have created urgent pressure for automation. “We focus on practical, pragmatic AI that delivers real ROI—not hype,” he noted. Practical Automation Across the Service Lifecycle Acumen provides end-to-end observability and automation using real-time data and ML. It is vendor-agnostic, spans OSI layers 0–7, and includes a low-code/no-code Builder that allows Ribbon to tailor automation workflows and chatbots to each customer's environment. Real Deployments Already Underway Ribbon is working with several tier-one operators, including a major mobile provider moving from 4G to 5G across a multi-vendor network. Acumen is helping automate fault management, speed root-cause analysis, and proactively inform customer-facing teams. “It's not just fixing issues faster—it's keeping everyone, including the customer, informed,” Ramanathan said. Looking Ahead Ramanathan cautions organizations to avoid AI hype by setting realistic expectations and focusing on high-ROI outcomes first. “Break it into stages and show progress along the way,” he advised. Learn more at ribboncommunications.com.

In this episode of The 20/20 Podcast, Dr. Harbir Sian reconnects with returning guest and dry-eye expert Dr. Claudine Courey, recorded live at the OSI Summit at White Oaks Resort in Niagara-on-the-Lake. The conversation is completely unscripted — a candid mix of clinical pearls, entrepreneurial insight, and authentic reflections on optometry, business, and life.Dr. Courey shares the latest from Eye Drop Shop, including its partnership with OSI and the launch of Rinsada, a new in-office ocular-surface rinse now available in Canada. They discuss how Eye Drop Shop empowers optometrists to retail dry-eye and clean-beauty products online without carrying inventory, creating new revenue streams and patient touch points.The conversation flows into business mindset, patient education, and the difference between selling and helping. Harbir and Claudine also swap perspectives on personal growth, risk-taking, and what it means to build an authentic optometry brand. The episode closes on themes of humility, gratitude, and balance — with Harbir reflecting on the podcast's 200-episode journey and Claudine reminding us that everything — good or bad — is temporary.Key TopicsPartnership between Eye Drop Shop and OSI GroupLaunch of Rinsada, a new in-office saline flush treatment for allergy and debris removalEmpowering ODs through e-commerce and passive-income tools (like Auto)The importance of patient touch points and staying top-of-mind onlineShifting from “selling” to presenting solutionsHarbir's behind-the-scenes story of how The 2020 Podcast beganHandling tough industry conversations and asking hard questionsMindset: accountability, resilience, and self-leadership in optometryWork-life balance, gratitude, and the role of support systemsFeatured GuestsDr. Claudine Courey, Optometrist & Founder of Eye Drop Shop (Montreal, QC)Dr. Harbir Sian, Optometrist, Speaker, Host of The 20/20 PodcastResources MentionedEye Drop Shop — dry-eye & clean-beauty products for clinics and patientsRinsada — new in-office ocular-surface rinse treatmentOSI Group — Optometric Services Inc. networkOtto Optics — integrated e-commerce solution for ODsQuotable Moments“We're not selling — we're giving patients solutions to their problems.” — Dr. Claudine Courey“If it's all my fault, it's also all up to me to fix it.” — Dr. Harbir Sian“Everything is temporary — whether it's good or bad.” — Dr. Claudine CoureyLove the show? Subscribe, rate, review & share! http://www.aboutmyeyes.com/podcast/

Send us a textEveryone online says the new Special Warfare “Zulu Course” is trash—so Peaches and Trent decided to light it up. This isn't a soft take or sanitized military PR moment. It's two retired operators roasting the chaos, the memes, and the ridiculous leadership gag orders that make no sense. Peaches calls out the “change fatigue” across the DOD, breaks down why the Zulu rollout will be rough, and drops truth bombs about command cluelessness, budget black holes, and the myth of the “company man.” If you can't handle sarcasm and honesty about how training actually works, go listen to something else.⏱️ Timestamps: 00:00 – Peaches calls out “Company Man” energy 05:30 – The Zulu Course meltdown begins 08:40 – Change fatigue & leadership chaos 13:00 – Meme wars and gag orders gone stupid 19:00 – Legal orders, gag orders, and OSI overreach 25:00 – Why the first 3 Zulu classes will be total chaos 33:00 – Training breakdown: what “advanced” really means (hint: nothing) 41:00 – Subsuface swimming & pre-dive prep 52:00 – “They're still cones” – Peaches vs. the pipeline 55:00 – Peaches' spicy take on AFSOC “air commandos” 1:02:00 – If the Wing's paying, Peaches is for sale

Send us a textA decorated OSI agent who helped capture Taliban fighters and aided disaster survivors should be building a life in his forties. Instead, Robert Condon has spent 12 years behind bars, sentenced to 30, while his mother—retired Toledo police officer Holly Yeager—keeps fighting a case she believes was built on pressure, politics, and broken process. We open the file and follow the twists: a drug ring investigation that put Robert at odds with command priorities, a single accuser whose SANE exam reportedly found no injuries consistent with her extreme account, and two more “victims” cultivated through interviews that steered words toward charges and dangled immunity for unrelated misconduct.Holly walks us through the evidence gaps that still haunt the record: a second phone noted but never collected, weeks of exculpatory messages lost when Robert's device was destroyed after chain-of-custody issues, and discovery that surfaced a concealed felony history too late to test at trial. We talk Article 32 anomalies, special victims counsel influence, and a panel of superiors deciding guilt under the shadow of congressional pressure. Non‑unanimous verdicts, repeated speedy‑trial slippage, and unsworn statements shaped a path to a 30‑year sentence far above average. On appeal, mismatched and sealed record-of-trial pages made it harder for judges to validate citations or see context, dimming the chance for dissent and relief.Beyond the legal maze lies a family's cost: a son who lost his thirties, a 92‑year‑old grandfather running out of road trips, and a parole process that hinges on treatment requiring admissions he won't make. Holly's message is blunt and humane: protect real survivors and protect due process. Stop manufacturing narratives to save weak cases. Build independent evidence integrity, require unanimous verdicts, insulate panels from command, and hold investigators to the same standards we demand in civilian courts.Listen, share, and weigh in with your perspective on military justice reform. If this story moved you, subscribe, leave a review, and send the episode to someone who cares about truth over optics.Support the showVisit my website: https://thehello.llc/THERESACARPENTERRead my writings on my blog: https://www.theresatapestries.com/Listen to other episodes on my podcast: https://storiesofservice.buzzsprout.comWatch episodes of my podcast:https://www.youtube.com/c/TheresaCarpenter76

The real edge in cybersecurity isn't found in new tools, it's built through timeless fundamentals and a mindset that never stops learning. In this episode, Ron sits down with Rich Greene, Senior Solutions Engineer and Instructor at SANS Institute, to uncover how true cyber value starts with skills, curiosity, and mindset. Rich shares his remarkable story of surviving a battlefield injury, retraining his brain, and how that journey shaped his approach to mastering cybersecurity. Together, they connect real-world lessons like the recent Discord breach to the core truth that even advanced systems depend on people who master the basics. Impactful Moments 00:00 - Introduction 02:00 - Discord breach and third-party risk 05:00 - Meet Rich Greene from SANS 06:00 - The power of mastering fundamentals 07:00 - Learning how to learn 08:30 - Rich's story of rebuilding his memory 11:00 - Forcing the brain to grow stronger 12:00 - Top skills that get you paid 14:00 - Skills that lead to fulfillment 16:00 - Fundamentals that fuel long-term success 17:00 - The OSI model decoded 20:00 - Why operating systems matter 21:00 - Security operations fundamentals 23:00 - Why cloud is the #1 must-learn skill 25:00 - Final advice: sharpen your fundamentals   Links Connect with our Rich on LinkedIn: https://www.linkedin.com/in/secgreene/ Check out our upcoming events: https://www.hackervalley.com/livestreams Join our creative mastermind and stand out as a cybersecurity professional: https://www.patreon.com/hackervalleystudio Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com Continue the conversation by joining our Discord: https://hackervalley.com/discord Become a sponsor of the show to amplify your brand: https://hackervalley.com/work-with-us/  

Phil Rossi Returns!Take a walk with me down Fascination Street as I get to know even more about Phil Rossi, as he and I catch since his last appearance 7 years ago. Phil is one of the ORIGINAL podcasters. He first started podcasting his unpublished speculative fiction novels twenty years ago! In this episode, I fanboy out a little bit as Phil is on the Mount Rushmore of podcasting in my opinion. Then we move on to discuss his first ever novel 'Crescent' and why he is re-releasing it with a few changes. Next he spills the beans that he is also working on a sequel to Crescent! A few years ago, Phil started a podcast called Don't Turn Around; which focuses on the scary and the paranormal in our everyday lives. So we talk a bit about that and why he started it. More recently, Phil started another podcast called It Came From The Web, where he introduces and comments on paranormal videos and sightings that he finds on the internet. Then, He teamed up with fellow old scool podcasters Tee Morris and Philippa Balentine to form Old Spirits Investigations; a YouTube show where these three investigators are exploring purported haunted areas. So, we discuss all three of these shows, why they got started, and what the listener or viewer can expect from these shows. Phil even shares some personal stories of spooky and unexplainable events that happened in his own life. Make sure you check out the 2 audio podcasts, as we as the video podcast on YouTube. Phil, Tee, and Philippa are the bee's knees!!!

  An Interview With Dan Walters Dan Walters/Bytetinker joins us after a long email chain to find a time. You'll understand why it was so hard in the interview. Jen met Dan at cyphercon.com where he runs an ISP hacking village/ward. We covered a lot of stuff at various levels of the OSI stack up. Here's just a short list of terms that came up: DOCSIS Quadrature_amplitude_modulation Some of the exploits we covered were part of Cable Haunt. While Dan for good reason did not provide explicit sites to get started with, you may have luck with archive.org Want to try this for yourself but still waiting for your federal funding to come in? cyphercon.com 2026 is happening and you can do this much more cheaply. It's happening April 1 & 2. Have comments or suggestions for us? Find us on twitter @unnamed_show,  or email us at show@unnamedre.com. Music by TeknoAxe (http://www.youtube.com/user/teknoaxe)

In this deep dive episode, we explore the evolution of networking with Avery Pennarun, Co-Founder and CEO of Tailscale. Avery shares his extensive journey through VPN technologies, from writing his first mesh VPN protocol in 1997 called “Tunnel Vision” to building Tailscale, a zero-trust networking solution. We discuss how Tailscale reimagines the OSI stack by... Read more »

In this deep dive episode, we explore the evolution of networking with Avery Pennarun, Co-Founder and CEO of Tailscale. Avery shares his extensive journey through VPN technologies, from writing his first mesh VPN protocol in 1997 called “Tunnel Vision” to building Tailscale, a zero-trust networking solution. We discuss how Tailscale reimagines the OSI stack by... Read more »

Send us a textForget the Instagram highlight reels—Peaches and Aaron are dropping the unfiltered, step-by-step hacks to survive the Air Force Special Warfare pipeline without becoming another quit statistic. This isn't “drink more water” fluff; it's the down-and-dirty, why-your-shins-are-dying, how-to-not-faceplant-on-the-IFT kind of episode. From running until you hate life, to fueling like a machine, to training until your friends think you've joined a cult, they break down exactly how to build durability, crush run times, and show up already dangerous. Plus, a SIG P320 scandal, why OTS Nashville is about to sell out, and how to tell if you're lying to yourself about being “ready.”